Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Other literature type, Article English OPEN
Jianguo Zhou; Xiaolei Xu; Xuejing Huo; Yushuo Li;
(2019)
  • Publisher: Multidisciplinary Digital Publishing Institute
  • Journal: Sustainability (issn: 2071-1050)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.3390/su11030650
  • Subject: TD194-195 | Renewable energy sources | sample entropy theory | combined model | TJ807-830 | extreme-point symmetric mode decomposition | GE1-350 | short-term wind power prediction | Environmental sciences | Environmental effects of industries and plants
    arxiv: Astrophysics::High Energy Astrophysical Phenomena | Physics::Space Physics | Physics::Atmospheric and Oceanic Physics | Astrophysics::Solar and Stellar Astrophysics

The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-p... View more
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